To learn useful behaviors from data of a wide range of tasks, we can employ goal-conditioned reinforcement learning (GCRL) [1]}, [2]}, where a policy is trained to attain a specified goal (e.g., indicated as an image) from the current state.
This makes it practical to learn from previously collected large datasets without explicit reward annotations and to share knowledge across tasks that exhibit similar physical phenomena.
However, it is usually difficult to learn goal-conditioned policies that can solve temporally extended tasks in zero shot, as such policies are typically only effective for short-horizon goals [3]}.
To solve the target tasks, we would need to transfer the learned knowledge to the testing environment and efficiently fine-tune the policy in an online manner.
